pacman::p_load(readxl,gifski,gapminder,plotly,gganimate,tidyverse)Hands-on Exercise 3B
3.1 Overview
When telling a visually-driven data story, animated graphics tend to attract the interest of the audience and make deeper impressions than static graphics.
In this hands-on exercise, I will create animated data visualisation by using gganimate and plotly r packages. At the same time, I will (i) reshape data using the tidyr package, and (ii) process, wrangle and transform data using the dplyr package.
3.1.1 Basic concepts of animation
When creating animations, the plot does not actually move. Instead, many individual plots are built and then stitched together as movie frames, just like an old-school flip book or cartoon. Each frame is a different plot when conveying motion, which is built using some relevant subset of the aggregate data. The subset drives the flow of the animation when stitched back together.
3.1.2 Terminology
Before we dive into the steps for creating an animated statistical graph, it’s important to understand some of the key concepts and terminology related to this type of visualization.
Frame: In an animated line graph, each frame represents a different point in time or a different category. When the frame changes, the data points on the graph are updated to reflect the new data.
Animation Attributes: The animation attributes are the settings that control how the animation behaves. For example, you can specify the duration of each frame, the easing function used to transition between frames, and whether to start the animation from the current frame or from the beginning.
Tip
Before making animated graphs, we will need to ascertain if it makes sense to go through the effort. An animated graphic may not be worth the time investment for exploratory data analysis. However, for a presentation, a few well-placed animated graphics can help an audience connect with the topic remarkably better than static counterparts.
3.2 Getting Started
3.2.1 Loading the R packages
Before we begin, we will install and launch the following R packages:
plotly, R library for plotting interactive statistical graphs.
gganimate, an ggplot extension for creating animated statistical graphs.
gifski converts video frames to GIF animations using pngquant’s fancy features for efficient cross-frame palettes and temporal dithering. It produces animated GIFs that use thousands of colors per frame.
gapminder: An excerpt of the data available at Gapminder.org. We will just use its country_colors scheme.
tidyverse, a family of modern R packages specially designed to support data science, analysis and communication task including creating static statistical graphs.
The code chunk is as follows:
3.2.2 Importing the data
In this hands-on exercise, the Data worksheet from GlobalPopulation Excel workbook will be used.
The code chunk below is used to import the Data worksheet:
col <- c("Country","Continent")
globalPop <- read_xls("data/GlobalPopulation.xls",
sheet="Data") %>%
mutate_each_(funs(factor(.)),col) %>%
mutate(Year = as.integer(Year))
Things to learn from the code chunk above
read_xls()of readxl package is used to import the Excel worksheet.mutate_each_()of dplyr package is used to convert all character data type into factor.mutateof dplyr package is used to convert data values of Year field into integer.
However, as mutate_each_() was deprecated in dplyr 0.7.0. and funs() was deprecated in dplyr 0.8.0, we will re-write the code using mutate_at() as shown in the code chunk below:
col <- c("Country","Continent")
globalPop <- read_xls("data/GlobalPopulation.xls",
sheet="Data") %>%
mutate_at(col,as.factor) %>%
mutate(Year = as.integer(Year))Alternatively, instead of using mutate_at(), across() can be used to derive the same outputs:
col <- c("Country","Continent")
globalPop <- read_xls("data/GlobalPopulation.xls",
sheet="Data") %>%
mutate_at(col,as.factor) %>%
mutate(Year = as.integer(Year))3.3 Animated Data Visualisation: gganimate methods
gganimate extends the grammar of graphics as implemented by ggplot2 to include the description of animation. It does this by providing a range of new grammar classes that can be added to the plot object in order to customise how it should change with time.
transition_*()defines how the data should be spread out and how it relates to itself across time.view_*()defines how the positional scales should change along the animation.shadow_*()defines how data from other points in time should be presented in the given point in time.enter_*()/exit_*()defines how new data should appear and how old data should disappear during the course of the animation.ease_aes()defines how different aesthetics should be eased during transitions.
3.3.1 Building a static population bubble plot
In the code chunk below, the basic ggplot2 functions are used to create a static bubble plot.
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young')+
ggtitle("Distribution of % Young vs % Old")
3.3.2 Building the animated bubble plot
In the code chunk below,
transition_time()of gganimate is used to create transition through distinct states in time (i.e. Year).ease_aes()is used to control easing of aesthetics. The default islinear. Other methods are: quadratic, cubic, quartic, quintic, sine, circular, exponential, elastic, back, and bounce.
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young') +
ggtitle("Distribution of % Young vs % Old")+
transition_time(Year) +
ease_aes('linear') The animated bubble chart is as follows:
3.4 Animated Data Visualisation: plotly
In Plotly R package, both ggplotly() and plot_ly() support key frame animations through the frame argument/aesthetic. They also support an ids argument/aesthetic to ensure smooth transitions between objects with the same id (which helps facilitate object constancy).
3.4.1 Building an animated bubble plot: ggplotly() method
In this sub-section, I will create an animated bubble plot by using ggplotly() method.
The animated bubble plot above includes a play/pause button and a slider component for controlling the animation
gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young')+
ggtitle("Distribution of % Young vs % Old")
ggplotly(gg)
Things to learn from the code chunk above
Appropriate ggplot2 functions are used to create a static bubble plot. The output is then saved as an R object called gg.
ggplotly()is then used to convert the R graphic object into an animated svg object.
Even though show.legend = FALSE argument was used, the legend still appears on the plot. To overcome this problem, theme(legend.position=‘none’) should be used as shown in the plot and code chunk below:
gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young') +
theme(legend.position='none')+
ggtitle("Distribution of % Young vs % Old")
ggplotly(gg)3.4.2 Building an animated bubble plot: plotly() method
In this sub-section, I will create an animated bubble plot by using plot_ly() method.
bp <- globalPop %>%
plot_ly(x = ~Old,
y = ~Young,
size = ~Population,
color = ~Continent,
sizes = c(2, 100),
frame = ~Year,
text = ~Country,
hoverinfo = "text",
type = 'scatter',
mode = 'markers'
) %>%
layout(showlegend = FALSE,title="Distribution of Young against Old")
bp3.5 References
Link to a senior’s work with interesting implementation of gganimate